GITNUXSOFTWARE REVIEW
Science ResearchTop 10 Best Agent Modeling Software of 2026
Compare agent modeling software options for policy, logistics, and transport research, with practical tradeoffs and fit notes for teams.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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GAMA Platform is the best pick for teams that need repeatable agent-based and spatial scenario runs because its native GAML spatial modeling ties agent behavior to environment updates in one coherent model artifact, whereas MATSim fits research groups focusing on transport experiments since iterative plan mutation, utility scoring, and QSim congestion make travel-choice realism scale to large populations.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
GAMA Platform
Native GAML spatial modeling with agent queries and environment interactions executes as a single coherent simulation model.
Best for: Fits when teams need repeatable agent-based and spatial scenario runs with model reuse across multiple experiments.
MATSim
Editor pickIterative plan mutation, utility scoring, and QSim execution connect traveler choices with network congestion at population scale.
Best for: Fits when research teams need configurable travel-demand experiments across large road and public-transit scenarios.
AnyLogic
Editor pickHybrid simulation modeling that coordinates agents with continuous and discrete event time behavior in one executable project.
Best for: Fits when teams need hybrid simulation models that mix agent logic with process flows.
Comparison Table
GAMA Platform
researchGAMA is an open-source modeling and simulation platform for spatially explicit agent-based systems.
Native GAML spatial modeling with agent queries and environment interactions executes as a single coherent simulation model.
GAMA Platform runs agent-based simulations from models written in the GAML language, with tight coupling between agent logic and environment behavior. It provides a simulation runtime that supports stepping, experiment orchestration, and data collection for scenario modeling.
Spatial modeling is built in through grid and continuous spaces that agents can query and update during execution. Tooling also supports model reuse via packages so teams can standardize behaviors across scenarios.
- +GAML language links agent behavior to environment updates in one model artifact
- +Built-in spatial grids and continuous spaces support common spatial scenarios
- +Experiment execution and dataset exports fit iterative calibration and comparisons
- +Package-based reuse supports shared agent types across model families
- –GAML learning curve is steeper than general agent frameworks
- –Advanced integrations depend on add-on ecosystems and external tooling
- –High-throughput runs can require careful tuning of observers and data collection
- –External API access is limited compared with agent frameworks built for web-native orchestration
Urban mobility analysts
Grid-based agent routing experiments
Faster policy comparison
Simulation engineering teams
Reusable agent logic via packages
Consistent model behavior
Show 1 more scenario
Logistics operations modelers
Continuous space environment updates
Clear interaction effects
Couple agents to environment behavior in continuous spaces and trace how interactions evolve over time.
Best for: Fits when teams need repeatable agent-based and spatial scenario runs with model reuse across multiple experiments.
MATSim
vertical specialistMATSim is an agent-based framework for large-scale transport and mobility simulations.
Iterative plan mutation, utility scoring, and QSim execution connect traveler choices with network congestion at population scale.
MATSim represents travelers with plans containing activities, routes, and transport modes. The Controler coordinates iterations, while QSim executes network movement and produces event streams for travel-time, congestion, and mode-use analysis. Java APIs and modular extensions support road traffic, public transit, freight, parking, emissions, and custom replanning logic.
MATSim requires substantial scenario preparation, including network cleaning, population generation, routing inputs, and parameter calibration. That overhead suits transport researchers and city planners testing congestion pricing, transit changes, road projects, or demand-management policies across large synthetic populations.
- +QSim models individual vehicle movements with configurable queue capacities and travel speeds
- +Controler coordinates routing, scoring, replanning, simulation, and output across iterations
- +Event handlers expose detailed movement, activity, transit, and congestion records
- +Java modules support custom agents, routing strategies, modes, and analysis pipelines
- –Scenario construction demands specialized transport data and domain knowledge
- –Java development is required for advanced behavioral or network extensions
- –Large populations can require substantial memory and distributed execution planning
- –Visual authoring and interactive debugging are limited compared with graphical simulators
Transport research teams
Evaluate congestion pricing policies
Policy effects across travelers
Public transit planners
Test route and schedule changes
Ridership and delay estimates
Show 2 more scenarios
City mobility offices
Assess road construction impacts
Evidence for transport planning
Network closures and capacity changes reveal rerouting, congestion propagation, travel-time changes, and affected activities.
Mobility data scientists
Generate synthetic travel events
Reusable simulation datasets
Event handlers export movement and activity records for emissions analysis, accessibility studies, and custom reporting.
Best for: Fits when research teams need configurable travel-demand experiments across large road and public-transit scenarios.
AnyLogic
enterpriseAnyLogic combines agent-based, discrete-event, and system dynamics modeling in one desktop platform.
Hybrid simulation modeling that coordinates agents with continuous and discrete event time behavior in one executable project.
AnyLogic is an agent-based and simulation modeling environment that combines agent logic with process models in one project. It supports visual model building, then compiles the model to run experiments for scenario comparison and sensitivity work.
AnyLogic’s distinct value comes from how it links agents to environment behavior and time-advance mechanics across continuous-time, discrete-event, and hybrid structures. It also offers integration points for pulling external data into simulation runs and pushing results back out for downstream analysis.
- +Multi-method simulation support in one model workspace
- +Strong agent-environment interaction patterns for operational scenarios
- +Experiment manager supports repeatable scenario runs and output collection
- +Integration options for importing data and exporting results
- –Complex hybrid models can be harder to debug than single-paradigm ones
- –Large models need careful performance tuning to keep throughput acceptable
- –API automation depth is narrower than general-purpose simulation ecosystems
- –Collaboration features can require extra governance to avoid model drift
Supply chain and transport teams
Discrete-event agent logistics trade-offs
Network trade-off insights
Warehouse design engineers
Material movement facility scenario variants
Material flow comparisons
Show 2 more scenarios
Game systems designers
Event timing and outcome regression tests
Earlier event validation
Simulate state-driven game events with randomized outcomes to validate timing and repeat logic behavior.
Operations process engineers
WIP and bottleneck diagnosis
WIP and bottleneck insight
Track entities through resources and buffers with routing decisions to diagnose flow performance constraints.
Best for: Fits when teams need hybrid simulation models that mix agent logic with process flows.
Simudyne
enterpriseSimudyne provides a commercial platform for large-scale agent-based simulations and scenario analysis.
Simudyne SDK parallel execution supports large parameter sweeps and structured comparison across simulation runs.
Engineering teams can define agents, environments, interactions, and rules in Java, then run controlled experiments through the Simudyne execution environment. Parallel runs support parameter sweeps and scenario comparison, while visual outputs help analysts inspect population behavior and aggregate results. The SDK structure suits organizations that need source-controlled models instead of a purely visual modeling interface.
- +Java SDK supports version-controlled model development
- +Parallel execution handles large scenario batches
- +Studio provides browser-based scenario and result management
- +Supports integration with enterprise data workflows
- –Requires Java development experience for custom models
- –Visual modeling coverage is narrower than code-first workflows
- –Model validation remains the customer’s responsibility
- –Advanced deployments require architecture and operations expertise
Financial risk teams
Bank stress scenario analysis
Comparable risk projections
Supply chain planners
Disruption response planning
Tested contingency strategies
Show 2 more scenarios
Public-sector analysts
Policy impact assessment
Evidence-based policy options
Analysts compare population responses to proposed interventions using configured rules and historical inputs.
Simulation engineers
Large experiment automation
Faster experiment throughput
Engineers define Java models, automate scenario batches, and compare outputs within controlled execution workflows.
Best for: Fits when enterprise teams need Java-based simulation with repeatable scenarios and controlled execution.
Repast
researchRepast provides open-source agent-based modeling toolkits for Java, Python, and distributed simulation.
Built-in spatial modeling with agent placement and neighborhood access routines designed for agent interaction logic.
Repast is an agent modeling and agent-based simulation toolkit with a workflow centered on writing agent rules and coupling them to an environment. It provides built-in support for agent scheduling, spatial layouts, and output hooks for tracing model behavior across runs.
- +Agent scheduling and spatial components reduce custom infrastructure work
- +Extensible agent and environment integration supports specialized models
- +Experiment output hooks make it practical to record runs for analysis
- +Scenario modeling workflow supports parameter sweeps and iterative refinement
- –Modeling requires code, so adoption slows for non-developers
- –GUI-based experimentation is limited compared with visual agent builders
- –Large-scale runs can demand careful attention to performance hotspots
Applied researchers
Reproducible calibration with iterations
Tighter parameter fits
Facility modeling teams
Spatial agent movement with layouts
Actionable behavior traces
Show 1 more scenario
Data-driven simulation analysts
External data coupling into runs
Better scenario realism
Integrate external inputs and custom agents using extension points during scenario modeling workflows.
Best for: Fits when modelers need code-driven control over agent interactions and repeatable simulation experiments.
Mesa
API-firstMesa is a Python framework for building, analyzing, and visualizing agent-based models.
Executable, documentation-driven scenario definitions that turn model revisions into repeatable experiment runs.
Mesa centers agent modeling around executable specifications, with behavior and experiment structure documented in a documentation-first workflow. It focuses on running multi-agent scenarios from configuration and code examples, then iterating based on observed outcomes rather than editing a UI graph.
- +Documentation-first examples make simulation setup reproducible across runs
- +Python workflow supports custom agent logic and experiment scripting
- +Scenario-based runs keep model changes tied to versioned code artifacts
- +Configuration-driven execution simplifies batch experimentation
- –Limited native tooling for spatial or network environment modeling
- –Multi-agent messaging abstractions require custom glue code
- –Built-in analysis utilities cover basics but require external tooling for deeper validation
- –Governance features like RBAC and audit logs are not a core focus
Python experiment developers
Config-driven multi-agent scenario runs
Repeatable experiments
Modeling teams prioritizing docs
Executable specifications with iteration
Faster model refinement
Show 1 more scenario
Research groups prototyping simulations
Component-based agent composition
Modular experiment building
Compose small components into larger experiments using a Python workflow to scale scenario testing.
Best for: Fits when builders need configuration-driven multi-agent experiments with Python customization.
FLAME GPU
API-firstFLAME GPU is a GPU-accelerated framework for simulating large populations of autonomous agents.
GPU-accelerated agent execution that targets large-scale spatial interaction models, not small interactive demos.
FLAME GPU is an agent modeling software solution built around GPU execution of large agent populations and spatial interaction rules. It supports authoring agent logic as models that run against environment inputs like fields and spatial geometry, then outputs state for analysis and replay. The distinct focus is high-throughput, data-parallel simulation with a workflow that starts from scenario inputs and ends with measurable agent and environment outcomes.
- +GPU execution path supports high agent counts for interaction-heavy simulations.
- +Spatial environment integration enables field and geometry driven agent behaviors.
- +Agent-to-environment update model keeps interaction logic close to simulation code.
- +Exportable simulation outputs support offline analysis and plotting pipelines.
- –Model authoring requires programming knowledge for agent logic and environment setup.
- –Debugging and validation are harder at GPU scale than in CPU-only prototypes.
- –Complex scenario assembly can require multiple tooling steps before repeatable runs.
Performance-focused simulation teams
GPU execution for large agent populations
Higher throughput simulation
Crowd and mobility analysts
Spatial geometry interaction modeling
Detailed state outputs
Show 1 more scenario
Simulation validation teams
Scenario input to measurable outcomes
Clear scenario outcomes
Start from scenario inputs, then produce measurable agent and environment outcomes for comparison.
Best for: Fits when teams need GPU-scale spatial agent simulation with repeatable scenario inputs and measurable outputs.
UrbanSim
vertical specialistUrbanSim is an open-source platform for agent-based urban development and land-use simulation.
Orca's table-and-column workflow lets modelers compose urban simulation steps from Python without changing the core engine.
UrbanSim represents households, persons, jobs, firms, buildings, and parcels as linked records that change during model runs. Orca coordinates table and column dependencies, while Pandana supports network-based accessibility calculations. Regional teams can also ingest synthetic populations and build custom model steps with Python.
- +Parcel, household, job, and building entities support fine-grained land-use scenarios
- +Python APIs expose model steps, tables, and data transformations
- +Orca separates workflow orchestration from model logic
- +Pandana adds network accessibility calculations for spatial analysis
- –Requires Python, geospatial data engineering, and model calibration skills
- –Documentation and examples favor technical users over planning analysts
- –Native collaboration controls and RBAC are not core capabilities
- –Built-in conversational agent orchestration and agent messaging are outside its primary design
Regional planning teams
Zoning policy scenarios
Parcel-level policy comparisons
Transportation researchers
Accessibility and land-use forecasts
Integrated transport projections
Show 1 more scenario
Urban data scientists
Custom model pipelines
Reproducible regional models
Python modules connect spatial datasets, model steps, scenario inputs, and repeatable simulation runs.
Best for: Fits when metropolitan planning teams need extensible parcel-level scenarios in Python.
AgentPy
API-firstAgentPy is a Python framework for agent-based modeling with experiment management and analysis tools.
AgentPy’s built-in model execution and parameter sweep workflow integrates experiment management into the simulation API.
AgentPy is a Python-first agent-based modeling library that turns simulation logic into importable, testable code. It provides core primitives for agents, grids and networks, time-stepped execution, and data collection across runs.
- +Python-native model code encourages version control and unit testing
- +Built-in data collection supports aggregating run outputs and distributions
- +Spatial and network modeling primitives reduce custom scaffolding
- +Batch parameter runs fit calibration and sensitivity workflows
- –Complex environment dynamics often require manual extension code
- –Large-scale performance depends on user efficiency in Python loops
- –Advanced multi-level scheduling requires deeper understanding of the engine
Operations analytics researchers
Time-stepped agent modeling in code
Structured metric collection
Batch experimentation teams
Parameter-driven scenario sweeps
Systematic scenario coverage
Show 1 more scenario
Prototype to runnable workflow teams
Example-to-research conversion
Runnable simulation pipeline
Translate example scripts into a repeatable research workflow that runs multi-agent scenarios end-to-end.
Best for: Fits when Python teams need reproducible agent-based simulation code with repeatable experiments.
MASON
researchMASON is a fast Java-based multi-agent simulation library with optional visualization components.
MASON’s scheduling subsystem lets models swap between time-stepped stepping and event-driven style execution.
MASON is an agent modeling software built for running multi-agent simulations with explicit control over scheduling, time advancement, and interaction rules. It provides a Java-based framework for defining agent classes, modeling environments, and stepping the simulation loop with pluggable schedules.
- +Fine-grained scheduling control over simulation steps and event timing
- +Java framework integrates directly with custom agent and environment code
- +Built-in spatial and environment utilities reduce core simulation boilerplate
- +Deterministic runs are practical via controllable random number generation
- –Java-centric workflow limits teams that prefer Python or web-first stacks
- –No native visual editor for agent behaviors beyond code and scripts
- –Higher effort to implement advanced communication protocols between agents
Distributed systems researchers
Explicit scheduling for interaction order
Deterministic interaction control
Simulation test engineers
Event timing and field-based dynamics
Accurate event sequencing
Show 1 more scenario
Experimental modeling teams
Reproducible randomness and metric recording
Consistent experiment outputs
Use built-in pseudo-random behavior for reproducibility and record metrics during simulation execution.
Best for: Fits when Java teams need deterministic, code-defined agent simulations with custom scheduling and experiment loops.
Conclusion
After evaluating 10 agent modeling software, GAMA Platform stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Agent Modeling Software
Agent modeling software is used to run multi-agent simulations where agent logic, environment state, and interaction rules evolve across controlled scenario runs. This guide covers MATSim, Simudyne, UrbanSim, AnyLogic, GAMA Platform, Repast, AgentPy, Mesa, MASON, and FLAME GPU based on their reported execution models and workflow constraints.
The tool set includes network-scale traveler choice simulation in MATSim, Java-based scenario execution in Simudyne and MASON, and table-driven urban modeling in UrbanSim’s Orca workflow. It also includes hybrid continuous and discrete behavior coordination in AnyLogic, spatial model artifacts in GAMA Platform and Repast, and Python experiment management in AgentPy and Mesa.
Agent Modeling Software for controlled multi-agent simulation runs
Agent modeling software provides an engine for executing agent schedules, time progression, and environment updates under repeatable scenario inputs. MATSim connects traveler choice replanning with QSim network execution so routing, scoring, and iteration produce measurable congestion and travel-demand effects at population scale.
UrbanSim’s Orca workflow composes parcel, household, job, and building steps in Python while pushing model step composition into table and column transformations exposed through Python APIs. These differences matter when selecting an environment modeling approach, since spatial grids and continuous spaces in GAMA Platform and built-in spatial routines in Repast change how environment state is represented and queried compared with code-driven or table-centric workflows.
Agent execution, scenario reproducibility, and automation surfaces
Agent modeling software separates simulation execution from experiment setup so teams can run repeatable scenario inputs and compare outcomes across controlled iterations. The strongest tools connect agent decisions to environment updates with an execution loop that supports batching, replanning, or scheduling across many runs.
Iteration loop that couples agent choices to environment state
MATSim connects traveler choice replanning to QSim network execution so routing, scoring, and replanning operate across iterations with congestion feedback. AnyLogic coordinates agent logic with hybrid continuous and discrete event time behavior in one executable project for process-style operational scenarios.
Batch execution and parameter sweep throughput
Simudyne SDK parallel execution supports large parameter sweeps across structured simulation runs using Java-based model code. AgentPy integrates experiment management into the simulation API so parameter sweeps can run from Python model code with built-in data collection.
Scenario composition workflow that controls model steps
UrbanSim’s Orca workflow lets modelers compose urban simulation steps from Python while the engine consumes table and column transformations exposed through Python APIs. Mesa uses documentation-driven scenario definitions that turn model revisions into repeatable experiment runs with Python customization.
Spatial environment representation and agent-environment interaction model
GAMA Platform packages GAML spatial modeling with agent queries and environment interactions into a single coherent model artifact. Repast provides spatial modeling routines for agent placement and neighborhood access, which reduces custom infrastructure for interaction logic.
Scheduling and time progression control
MASON includes a scheduling subsystem that can swap between time-stepped stepping and event-driven style execution, which supports deterministic agent timing. MATSim’s Controler coordinates routing, scoring, replanning, simulation, and output across iterations while QSim executes individual vehicle movements.
Scale-focused execution path for high agent counts
FLAME GPU targets GPU-accelerated agent execution for large-scale spatial interaction models with measurable outputs at high agent counts. MATSim targets population-scale transport experiments where throughput comes from network execution tied to traveler choice mutation and QSim queue capacity settings.
Pick the execution model that matches how scenarios and agent decisions must evolve
Agent modeling projects fail most often when the tool’s execution loop does not match the required time progression and scenario mutation workflow. The right choice depends on whether agent decisions must feed back into network dynamics across many replanning iterations or whether the model needs hybrid continuous and discrete behavior in one project.
Choose based on iteration style and feedback from congestion or environment dynamics
Select MATSim when traveler choice replanning must connect to QSim network congestion effects using a Controler-managed loop across iterations. Select AnyLogic when the model must coordinate hybrid continuous time processes with discrete event logic inside one executable project.
Choose based on how much scenario orchestration must be parallelized
Select Simudyne when large parameter sweeps require parallel execution across many runs with Java-based model control. Select AgentPy when Python teams want experiment management integrated into the model execution API for reproducible sweeps and run output aggregation.
Choose the scenario composition boundary: tables and columns versus code-defined experiments
Select UrbanSim when parcel, household, job, and building entities must be expressed through Python-exposed tables and columns in Orca step composition. Select Mesa when documentation-first scenario definitions must turn model revisions into repeatable experiment runs with Python customization.
Choose the environment modeling shape: unified spatial artifact versus code-driven spatial routines
Select GAMA Platform when repeatable spatial scenario runs must compile into one model artifact with GAML linking agent behavior to environment updates. Select Repast when code-driven spatial interaction logic needs built-in routines for agent placement and neighborhood access to reduce custom spatial infrastructure work.
Choose based on time control and determinism requirements
Select MASON when deterministic agent simulations require explicit control over whether execution is time-stepped or event-driven. Select MATSim when time progression is driven by network execution mechanics like queue capacities and travel speeds tied to iterative routing and scoring.
Choose when GPU scale is required for interaction-heavy spatial models
Select FLAME GPU when the model needs a GPU execution path for high agent counts and interaction-heavy spatial scenarios with field and geometry driven behaviors. Select tools like Repast or GAMA when the expected scale does not require a GPU execution model and spatial queries can run within a CPU-based simulation artifact.
Which teams get the most leverage from each agent modeling workflow
Different tools fit different modeling teams because the authoring language, execution loop, and orchestration boundary change where engineering effort goes. The best match appears when the team can adopt the tool’s native workflow rather than wrapping it around custom infrastructure.
Transport research teams running network-scale traveler choice experiments
MATSim fits when experiments require iterative plan mutation and QSim execution that connects traveler decisions to network congestion at population scale.
Enterprise engineering teams building code-first simulation libraries in Java
Simudyne fits when Java-based simulation development must support version-controlled model development and parallel execution for large scenario batches.
Metropolitan planning teams composing parcel-level scenarios in Python
UrbanSim fits when extensible parcel, household, job, and building entities must be expressed through Orca step composition with Python APIs and data transformations.
Modeling teams that need hybrid continuous and discrete process coordination
AnyLogic fits when operational scenarios require continuous and discrete behavior coordination within one executable project rather than separate tooling.
GPU-oriented teams testing high agent-count spatial interactions
FLAME GPU fits when the workload targets GPU-accelerated agent execution for interaction-heavy spatial models with measurable high-throughput outputs.
Common ways agent modeling projects lose time during setup and validation cycles
Teams often select a tool based on agent simulation marketing terms and then hit workflow mismatches in scenario authoring, time progression, or environment representation. The most costly mistakes show up when model execution requires domain-specific data plumbing that the team does not already have.
Choosing a network-scale transport tool without the specialized transport data required for MATSim scenarios
MATSim requires specialized transport data and domain knowledge for scenario construction. Plan for Java development if advanced behavioral or network extensions must be implemented.
Building a complex hybrid simulation without a debugging plan for AnyLogic hybrid models
AnyLogic hybrid models can be harder to debug than single-paradigm ones because continuous and discrete behaviors interact. Large models also need performance tuning to keep throughput acceptable.
Assuming visual experimentation exists for MASON when the workflow is code-centric
MASON is Java-centric and relies on code for agent and environment logic, which slows adoption for teams that avoid development. There is no native visual editor beyond code and scripts.
Underestimating the spatial modeling integration cost when spatial and network needs exceed native tooling
Mesa provides limited native tooling for spatial or network environment modeling and multi-agent messaging abstractions require custom glue code. Repast includes built-in spatial routines for neighborhood access, which reduces custom infrastructure work.
Treating GPU acceleration as a drop-in substitute for CPU debugging when validating FLAME GPU models
FLAME GPU debugging and validation are harder at GPU scale than in CPU-only prototypes. Model authoring still requires programming knowledge for agent logic and environment setup.
How We Selected and Ranked These Tools
We evaluated each tool for execution-loop fit, scenario reproducibility mechanics, and the practical automation surface used to run controlled scenario experiments. Features accounted for simulation workflow depth and iteration mechanics, ease/value accounted for how direct the model authoring and experiment setup feel for the dominant language in each tool.
MATSim separated itself by connecting iterative plan mutation with QSim network execution so routing, scoring, replanning, and congestion effects run together in a Controler-managed loop at population scale. We also treated parallel execution support as a throughput differentiator in Simudyne and treated Python-first scenario step composition as a differentiator in UrbanSim’s Orca workflow.
Frequently Asked Questions About Agent Modeling Software
How do CrewAI, AutoGen, and Microsoft Semantic Kernel differ from agent modeling toolkits like AnyLogic and Mesa?
When does MATSim’s queue-based network simulation become more appropriate than Monte Carlo sweeps in AgentPy?
Which tool provides a hybrid time-advance approach for coordinating agents with both continuous and discrete event behavior?
How are APIs and integration surfaces handled when moving results into an analysis pipeline?
How do security controls like SSO and RBAC typically map to code-driven simulation platforms?
What breaks if scenario data migration is incomplete when moving a model between tools like UrbanSim and Repast?
How do admin controls and governance work when multiple teams reuse model components?
Where does FLAME GPU fall short compared with CPU-based frameworks like MASON or Simudyne when building detailed interaction logic?
How can extensibility be tested before building a full scenario library in GAMA Platform or MASON?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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